Mimecast CyberGraph to Help Defend Against Sophisticated Email Threats

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Mimecast has announced the Mimecast CyberGraph solution, which is a new add-on for Mimecast Secure Email Gateway (SEG).

The new solution is built to use Artificial Intelligence (AI) to help detect sophisticated phishing and impersonation attacks.

CyberGraph creates an identity graph which is built to store information about relationships between all senders and recipients. The graph is designed to detect anomalies and leverages machine learning technology to help organizations stay one step ahead of threat actors by alerting employees to potential cyber threats.

Josh Douglas, Mimecast
Josh Douglas, Mimecast

Josh Douglas, VP, Product Management for Threat Intelligence at Mimecast, said, “Phishing and impersonation attacks are getting more sophisticated, personalized and harder to stop. If not prevented, these attacks can have devastating results for an enterprise organisation.”

He added that security controls need to be constantly updated and improved to outsmart threat actors.

“CyberGraph leverages our AI and machine learning technologies to help keep employees one step ahead with real-time warnings, directly at the point of risk.

“What makes this exciting is that we are embedding the technology for existing email security customers, they do not need to look for other vendors to fill the gap with technologies that only work to solve part of this challenge.”

The workplace is always the top target of cybercriminals, but in the remote working era, the problem has intensified. “The State of Email Security Report” found that email threats rose by 64% and employees are clicking on three times as many malicious emails as they had before the COVID-19 pandemic. Security controls need to evolve to help evade cybercriminals’ relentless and crafty approach.

CyberGraph includes key capabilities to help prevent cyber threats. During the reconnaissance phase of an attack, threat actors embed trackers into emails that communicate with an illegitimate remote server, disclosing important information that can be used to create a targeted social engineering attack.  CyberGraph is built to blocks this communication, mask the email recipient’s location, and prevents attempts to understand engagement levels with the email content.

It is designed to create an identity graph by learning about relationships and connections between all senders and recipients. This intelligence is combined with the outputs from machine learning models to detect anomalies that could be indicative of a malicious email.

CyberGraph is engineered to engage users at the point of risk with color-coded banners that indicate the potential nature of a threat. Users are empowered by seeing whether an email is safe or potentially nefarious.

The solution is built to “crowd-sources” threat intelligence, which helps to reinforce the machine learning model. As the risk associated with any given delivered email changes, banners embedded in any similar emails are updated with the latest information, providing ongoing engagement and protection for users.

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